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A deep learning pproach for the morphological recognition of reactive lymphocytes in patients with COVID-19 infection
dc.contributor.author | Rodellar Benedé, José |
dc.contributor.author | Barrera Llanga, Kevin Iván |
dc.contributor.author | Alférez Baquero, Edwin Santiago |
dc.contributor.author | Boldú Nebot, Laura |
dc.contributor.author | Laguna Moreno, Javier |
dc.contributor.author | Molina Borrás, Ángel |
dc.contributor.author | Merino González, Anna |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Matemàtiques |
dc.date.accessioned | 2022-07-21T12:06:02Z |
dc.date.available | 2022-07-21T12:06:02Z |
dc.date.issued | 2022-05-23 |
dc.identifier.citation | Rodellar, J. [et al.]. A deep learning pproach for the morphological recognition of reactive lymphocytes in patients with COVID-19 infection. "Bioengineering", 23 Maig 2022, vol. 9, núm. 229, p. 1-20. |
dc.identifier.issn | 2306-5354 |
dc.identifier.uri | http://hdl.handle.net/2117/370833 |
dc.description.abstract | Laboratory medicine plays a fundamental role in the detection, diagnosis and management of COVID-19 infection. Recent observations of the morphology of cells circulating in blood found the presence of particular reactive lymphocytes (COVID-19 RL) in some of the infected patients and demonstrated that it was an indicator of a better prognosis of the disease. Visual morphological analysis is time consuming, requires smear review by expert clinical pathologists, and is prone to subjectivity. This paper presents a convolutional neural network system designed for automatic recognition of COVID-19 RL. It is based on the Xception71 structure and is trained using images of blood cells from real infected patients. An experimental study is carried out with a group of 92 individuals. The input for the system is a set of images selected by the clinical pathologist from the blood smear of a patient. The output is the prediction whether the patient belongs to the group associated with better prognosis of the disease. A threshold is obtained for the classification system to predict that the smear belongs to this group. With this threshold, the experimental test shows excellent performance metrics: 98.3% sensitivity and precision, 97.1% specificity, and 97.8% accuracy. The system does not require costly calculations and can potentially be integrated into clinical practice to assist clinical pathologists in a more objective smear review for early prognosis. |
dc.format.extent | 20 p. |
dc.language.iso | eng |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
dc.subject.lcsh | Artificial intelligence--Engineering applications |
dc.subject.lcsh | COVID-19 (Disease) |
dc.subject.lcsh | Bioengineering |
dc.subject.other | Deep learning |
dc.subject.other | convolutional neural networks |
dc.subject.other | COVID-19 |
dc.subject.other | Blood cell images |
dc.subject.other | Cell morphology |
dc.subject.other | Reactive lymphocytes |
dc.subject.other | Diagnosis |
dc.subject.other | Prognosis |
dc.title | A deep learning pproach for the morphological recognition of reactive lymphocytes in patients with COVID-19 infection |
dc.type | Article |
dc.subject.lemac | Intel·ligència artificial--Aplicacions a l'enginyeria |
dc.subject.lemac | COVID-19 (Malaltia) |
dc.subject.lemac | Bioenginyeria |
dc.contributor.group | Universitat Politècnica de Catalunya. CoDAlab - Control, Modelització, Identificació i Aplicacions |
dc.identifier.doi | 10.3390/bioengineering9050229 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://www.mdpi.com/2306-5354/9/5/229 |
dc.rights.access | Open Access |
local.identifier.drac | 33756857 |
dc.description.version | Postprint (published version) |
dc.relation.projectid | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-104087RB-I00/ES/HEMATOPATOLOGIA COMPUTACIONAL: SOLUCIONES DE APRENDIZAJE PROFUNDO PARA EL DIAGNOSTICO DE ENFERMEDADES HEMATOLOGICAS A PARTIR DE IMAGENES DE CELULAS DE SANGRE PERIFERICA/ |
local.citation.author | Rodellar, J.; Barrera, K.; Alferez, E.; Boldú, L.; Laguna, J.; Molina, Á.; Merino, A. |
local.citation.publicationName | Bioengineering |
local.citation.volume | 9 |
local.citation.number | 229 |
local.citation.startingPage | 1 |
local.citation.endingPage | 20 |
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